3D scanning tools are often used to scan a 3D surface to generate corresponding 3D point clouds. These point clouds are then typically used for constructing 3D point- or mesh-based digital models of the scanned surface. Unfortunately, due to many possible sources of noise during the scanning process, the resulting 3D models tend to be noisy.
A number of conventional techniques for removing noise while attempting to preserve underlying features of such models have been developed. In most cases, denoising of the sampled data or 3D mesh or model can be applied either before or after generating the model.
Techniques such as mesh smoothing operate by filtering or otherwise adjusting a 3D input surface to increase a degree of smoothness of that surface by denoising the data representing that surface. For example, one recent technique provides a bilateral denoising filter for 3D point clouds that operates by filtering vertices of a corresponding 3D mesh. This technique generally filters vertices in the normal direction using local neighborhoods to denoise the mesh while partially preserving local features. Unfortunately, existing techniques for smoothing or denoising 3D surfaces, models, meshes or point clouds tend to remove noise while partially blurring out fine details of 3D features. Further, these methods tend to produce at least some degree of shrinkage or drifting of the 3D surfaces.